internal static Model PatchModel(Model model, string[] additionalOutputs, string[] trimOutputs = null)
{
bool trimModel = trimOutputs != null;
if (model.flags.HasFlag(Model.Flags.NeedsCompilation))
{
model.Compile();
}
if (trimOutputs != null)
{
foreach (var o in trimOutputs.Except(model.outputs))
{
if (additionalOutputs == null || !additionalOutputs.Contains(o))
{
D.LogWarning($"Output specified in trimOutputs was not found in the model: {o}");
}
}
var newModel = model.ShallowCopy();
newModel.outputs = trimOutputs.Intersect(model.outputs).ToList();
model = newModel;
}
if (additionalOutputs != null)
{
foreach (var o in additionalOutputs.Except(model.layers.Select(l => l.name)))
{
D.LogWarning($"Layer specified in additionalOutputs was not found in the model: {o}");
}
// 'new' means that output name does not yet exist in model.outputs
// 'valid' means that output name matches one of the existing model.layer names
var newAndValidAdditionalOutputs =
additionalOutputs.Except(model.outputs).Intersect(model.layers.Select(l => l.name));
var newModel = model.ShallowCopy();
newModel.outputs.AddRange(newAndValidAdditionalOutputs);
model = newModel;
}
if (trimModel)
{
var newModel = model.ShallowCopy();
var upstream = ModelAnalyzer.FindUpstreamLayers(model, newModel.outputs.ToArray());
foreach (var l in model.layers)
{
if (!upstream.Contains(l))
{
newModel.layers.Remove(l);
}
}
model = newModel;
}
model = ModelOptimizer.RemoveNoop(model);
return(model);
}
private static Layer.FusedActivation GetAndVerifyFusedActivation(Layer l)
{
Assert.IsTrue(ModelOptimizer.IsLayerSupportingActivationFusing(l.type));
if (!ModelOptimizer.IsActivationFusable(l.activation))
{
throw new NotImplementedException("This activation function is not implemented as a fusable one yet! Check Layer.FusedActivation for supported ones.");
}
return((Layer.FusedActivation)l.activation);
}
private static void PatchLayer(List <Layer> layers, Layer layer)
{
// Split Load so that each constant tensor gets its own layer
// for the sake of simplicity of the execution code
if (layer.type == Layer.Type.Load &&
layer.datasets.Length > 1)
{
foreach (var t in layer.datasets)
{
Layer layerC = new Layer(t.name, Layer.Type.Load); // load using tensor name
layerC.inputs = layer.inputs;
layerC.datasets = new[] { t };
layers.Add(layerC);
}
// patch original layer
layer.name = layer.name + "_nop";
layer.type = Layer.Type.Nop;
layer.datasets = new Layer.DataSet[] {};
}
// Split activation part into separate layer when activation fusing is not supported.
// NOTE: Keras specific. Only Keras exporter packs both Dense/Conv and Activation into the same layer.
// @TODO: move layer split directly into Keras exporter
if (layer.type != Layer.Type.Activation &&
layer.activation != Layer.Activation.None &&
(!ModelOptimizer.IsLayerSupportingActivationFusing(layer.type) || !ModelOptimizer.IsActivationFusable(layer.activation)))
{
var affineOutput = layer.name + "_tmp";
Layer layerA = new Layer(layer.name, layer.activation); // take the original layer name
layerA.inputs = new[] { affineOutput };
// patch original layer
layer.name = affineOutput;
layer.activation = Layer.Activation.None;
Assert.AreEqual(layers[layers.Count - 1].name, layer.name);
Assert.AreEqual(layers[layers.Count - 1].activation, layer.activation);
layers.Add(layerA);
}
// @TODO: Enable Dropout
// @TEMP: disabled runtime Dropout noise to get more predictable results for auto testing
if (layer.type == Layer.Type.Dropout)
{
layer.type = Layer.Type.Activation;
layer.activation = Layer.Activation.None;
}
}
